- These data and analytics (D&A) trends will allow you to anticipate change and manage uncertainty.
- Investing in those trends that are most relevant to your organization can help you meet your CEO’s priority of returning to and accelerating growth.
- Proactively monitor, experiment with or then decide to aggressively invest in key trends based on their urgency and alignment to your strategic business priorities.
Russia’s invasion of Ukraine added a geopolitical crisis to the enduring global pandemic, and managing consequent and persistent uncertainty and volatility will be a key focus for data and analytics leaders this year.
Download now: The IT Roadmap for Data and Analytics
“Now is the time to anticipate, adapt and scale the value of your D&A strategy by monitoring, experimenting with or aggressively investing in key D&A technology trends based on their urgency and alignment to business priorities,” says Rita Sallam, Distinguished VP Analyst at Gartner.
This year’s top trends in data and analytics relate to three imperatives:
- Activate diversity and dynamism. Use adaptive AI systems to drive growth and innovation while coping with fluctuations in global markets.
- Augment people and decisions to deliver enriched, context-driven analytics created from modular components by the business.
- Institutionalize trust to achieve value from D&A at scale. Manage AI risk and enact connected governance across distributed systems, edge environments and emerging ecosystems.
12 data and analytics (D&A) trends on the radar in 2022
We've identified the data and analytics trends that represent business, market and technology dynamics that you cannot afford to ignore. These trends also help prioritize investments to drive new growth, efficiency, resilience and innovation.
Download now: 5 Key Iniatives to Becoming a Data-Driven Organization
No. 1: Adaptive AI systems
As decisions become more connected, contextual and continuous, it's increasingly important to reengineer decision making. You can do so by using adaptive AI systems, which can offer faster and flexible decisions by adapting more quickly to changes.
However, to build and manage adaptive AI systems, adopt AI engineering practices. AI engineering orchestrates and optimizes applications to adapt to, resist or absorb disruptions, facilitating the management of adaptive systems.
Download eBook: 5 Key Actions for IT Leaders to Make Better Decisions
No. 2: Data-centric AI
Many organizations attempt to tackle AI without considering AI-specific data management issues. “Without the right data, building AI is risky and possibly dangerous,” says Sallam. As such, it is critical to formalize data-centric AI and AI-centric data. They address data bias, diversity and labeling in a more systematic way as part of your data management strategy — including, for example, using data fabric in automated data integration and active metadata management.
No. 3: Metadata-driven data fabric
The data fabric listens, learns and acts on the metadata. It flags and recommends actions for people and systems. Ultimately, it improves trust in, and use of, data in the organization and can reduce by 70% various data management tasks, including design, deployment and operations.
As an example, the city of Turku in Finland foundgaps in its data held back its innovation. By integrating fragmented data assets, it was able to reuse data, reduce time to market by two-thirds and create a monetizable data fabric.
Learn more: Your Ultimate Guide to Data and Analytics
No. 4: Always share data
While data and analytics leaders often acknowledge that data sharing is a key digital transformation capability, they lack the know-how to share data at scale and with trust.
To succeed in promoting data sharing and increasing access to the right data aligned to the business case, collaborate across business and industry lines. This will accelerate buy-in for increased budget authority and investment in data sharing. In addition, consider adopting data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.
No. 5: Context-enriched analysis
Context-enriched analysis builds on graph technologies. The information on the user’s context and needs is held in a graph that enables deeper analysis using the relationships between data points as much as the data points themselves. It helps identify and create further context based on similarities, constraints, paths and communities.
Capturing, storing and using contextual data demands capabilities and skills in building data pipelines, X analytics techniques and AI cloud services that can process different data types. By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data.
No. 6: Business-composed D&A
Gartner champions a modular approach to data and analytics, or “composable D&A.” Business-composed data and analytics builds on this trend, but the focus is on the people side, shifting from IT to business.
Business-composed D&A enables the business users or business technologists to collaboratively craft business-driven data and analytics capabilities.
Learn more: Everything You Need to Know About Artificial Intelligence
No. 7: Decision-centric D&A
The discipline of decision intelligence, which is careful consideration of how decisions should be made, is causing organizations to rethink their investments in D&A capabilities. Use decision intelligence disciplines to design the best decision, and then deliver the required inputs.
Gartner estimates that by 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.